| Title | Improvement of Style Transfer Algorithm based on Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Jia, Ning, Gong, Xiaoyi, Zhang, Qiao |
| Conference Name | 2021 International Conference on Computer Engineering and Application (ICCEA) |
| Date Published | jun |
| Keywords | Deep Learning, distortion, Image color analysis, Image edge detection, image edge information fusion, image segmentation, Metrics, Neural networks, neural style transfer, pubcrawl, resilience, Resiliency, Scalability, semantic segmentation, Semantics, style transfer |
| Abstract | In recent years, the application of style transfer has become more and more widespread. Traditional deep learning-based style transfer networks often have problems such as image distortion, loss of detailed information, partial content disappearance, and transfer errors. The style transfer network based on deep learning that we propose in this article is aimed at dealing with these problems. Our method uses image edge information fusion and semantic segmentation technology to constrain the image structure before and after the migration, so that the converted image maintains structural consistency and integrity. We have verified that this method can successfully suppress image conversion distortion in most scenarios, and can generate good results. |
| DOI | 10.1109/ICCEA53728.2021.00008 |
| Citation Key | jia_improvement_2021 |
Improvement of Style Transfer Algorithm based on Neural Network